🚨 Master Lasso Regression: Simplifying L1 Regularization 📈

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What is Lasso Regression?
📉 Introduction to Lasso (Least Absolute Shrinkage and Selection Operator) as a linear regression technique with L1 regularization.

L1 Regularization Explained
🔑 Understand how L1 regularization adds a penalty term to reduce overfitting and select important features.

How Lasso Helps with Feature Selection
✂️ See how Lasso shrinks coefficients to zero, effectively removing irrelevant features for a cleaner model.

Advantages of Lasso Regression
✅ Key benefits like improved performance, reduced complexity, and handling high-dimensional data.

Formula Breakdown
🧮 A simple explanation of Lasso’s formula and how the L1 penalty works.

Lasso vs. Ridge Regression
⚖️ Compare Lasso and Ridge (L2 regularization), highlighting Lasso’s feature selection strength.

Practical Applications
🌍 Real-world use cases like gene data or text classification, where Lasso shines.

When to Use Lasso
🧐 Tips on knowing when Lasso is the best option for your dataset and model.

Conclusion
🎯 Summarize how Lasso Regression helps create efficient, interpretable models for better predictions.